Sam Kriegman

1.5k total citations · 1 hit paper
20 papers, 730 citations indexed

About

Sam Kriegman is a scholar working on Mechanical Engineering, Condensed Matter Physics and Biomedical Engineering. According to data from OpenAlex, Sam Kriegman has authored 20 papers receiving a total of 730 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Mechanical Engineering, 8 papers in Condensed Matter Physics and 7 papers in Biomedical Engineering. Recurrent topics in Sam Kriegman's work include Modular Robots and Swarm Intelligence (13 papers), Micro and Nano Robotics (8 papers) and Robot Manipulation and Learning (4 papers). Sam Kriegman is often cited by papers focused on Modular Robots and Swarm Intelligence (13 papers), Micro and Nano Robotics (8 papers) and Robot Manipulation and Learning (4 papers). Sam Kriegman collaborates with scholars based in United States, Italy and Hungary. Sam Kriegman's co-authors include Michael Levin, Josh Bongard, Douglas Blackiston, Rebecca Kramer‐Bottiglio, Dylan Shah, Simon Garnier, Joshua Bongard, Nick Cheney, Amir Mohammadi Nasab and Andrew Spielberg and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Advanced Materials and Science Robotics.

In The Last Decade

Sam Kriegman

18 papers receiving 705 citations

Hit Papers

A scalable pipeline for designing reconfigurable organisms 2020 2026 2022 2024 2020 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A scalable pipeline for designing reconfigurable organisms
    2020 275 citations Sam Kriegman, Douglas Blackiston et al. Proceedings of the National Academy of Sciences profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sam Kriegman United States 10 327 313 218 121 102 20 730
Oliver Pohl United States 9 201 0.6× 222 0.7× 378 1.7× 148 1.2× 107 1.0× 11 1.2k
Serge Kernbach Germany 13 139 0.4× 399 1.3× 106 0.5× 30 0.2× 19 0.2× 38 738
Nicolas Bredèche France 16 150 0.5× 311 1.0× 68 0.3× 115 1.0× 68 0.7× 59 934
Masoud Asadpour Switzerland 10 117 0.4× 218 0.7× 87 0.4× 40 0.3× 31 0.3× 16 625
Alexandre Campo Belgium 11 429 1.3× 305 1.0× 73 0.3× 48 0.4× 141 1.4× 27 1.0k
Jim Bellingham United States 5 452 1.4× 309 1.0× 240 1.1× 17 0.1× 110 1.1× 5 1.0k
Yasemin Ozkan-Aydin United States 15 462 1.4× 332 1.1× 194 0.9× 25 0.2× 23 0.2× 35 718
Atsushi Tero Japan 17 1.4k 4.3× 132 0.4× 50 0.2× 146 1.2× 119 1.2× 27 1.9k
Juan Cristóbal Zagal Chile 12 285 0.9× 238 0.8× 109 0.5× 27 0.2× 17 0.2× 25 578
Norbert Stoop Switzerland 16 253 0.8× 395 1.3× 184 0.8× 91 0.8× 29 0.3× 35 874

Countries citing papers authored by Sam Kriegman

Since Specialization
Citations

This map shows the geographic impact of Sam Kriegman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sam Kriegman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Kriegman more than expected).

Fields of papers citing papers by Sam Kriegman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sam Kriegman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sam Kriegman. The network helps show where Sam Kriegman may publish in the future.

Co-authorship network of co-authors of Sam Kriegman

This figure shows the co-authorship network connecting the top 25 collaborators of Sam Kriegman. A scholar is included among the top collaborators of Sam Kriegman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sam Kriegman. Sam Kriegman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Muhan, et al.. (2024). Reinforcement learning for freeform robot design. 8799–8806.
2.
Kriegman, Sam, et al.. (2024). Evolution and learning in differentiable robots. 1 indexed citations
3.
Shah, Dylan, et al.. (2024). Author Correction: A soft robot that adapts to environments through shape change. Nature Machine Intelligence. 6(4). 493–493.
4.
Blackiston, Douglas, Sam Kriegman, Josh Bongard, & Michael Levin. (2023). Biological Robots: Perspectives on an Emerging Interdisciplinary Field. Soft Robotics. 10(4). 674–686. 19 indexed citations
5.
Spielberg, Andrew, et al.. (2023). Efficient automatic design of robots. Proceedings of the National Academy of Sciences. 120(41). e2305180120–e2305180120. 14 indexed citations
6.
Blackiston, Douglas, et al.. (2021). A cellular platform for the development of synthetic living machines. Science Robotics. 6(52). 110 indexed citations
7.
Kriegman, Sam, Amir Mohammadi Nasab, Douglas Blackiston, et al.. (2021). Scale invariant robot behavior with fractals. 8 indexed citations
8.
Kriegman, Sam, Douglas Blackiston, Michael Levin, & Josh Bongard. (2021). Kinematic self-replication in reconfigurable organisms. Proceedings of the National Academy of Sciences. 118(49). 73 indexed citations
9.
10.
Kriegman, Sam, Douglas Blackiston, Michael Levin, & Josh Bongard. (2020). A scalable pipeline for designing reconfigurable organisms. Proceedings of the National Academy of Sciences. 117(4). 1853–1859. 275 indexed citations breakdown →
11.
Shah, Dylan, et al.. (2020). Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization. Advanced Materials. 33(19). e2002882–e2002882. 118 indexed citations
12.
Kriegman, Sam, Amir Mohammadi Nasab, Dylan Shah, et al.. (2020). Scalable sim-to-real transfer of soft robot designs. 359–366. 47 indexed citations
13.
Kriegman, Sam, et al.. (2020). Morphology dictates learnability in neural controllers. 52–59. 3 indexed citations
14.
Liu, Sida, David R. Matthews, Sam Kriegman, & Josh Bongard. (2020). Voxcraft-sim, a GPU-accelerated voxel-based physics engine. Zenodo (CERN European Organization for Nuclear Research). 4 indexed citations
15.
Kriegman, Sam, et al.. (2019). Word2vec to behavior: morphology facilitates the grounding of language in machines. arXiv (Cornell University). 4. 4153–4160. 4 indexed citations
16.
Kriegman, Sam. (2019). Why virtual creatures matter. Nature Machine Intelligence. 1(10). 492–492. 7 indexed citations
17.
Kriegman, Sam, et al.. (2018). Interoceptive robustness through environment-mediated morphological development. Proceedings of the Genetic and Evolutionary Computation Conference. 109–116. 12 indexed citations
18.
Kriegman, Sam, et al.. (2018). The effects of morphology and fitness on catastrophic interference. 606–613. 3 indexed citations
19.
Kriegman, Sam, et al.. (2017). Simulating the evolution of soft and rigid-body robots. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1117–1120. 9 indexed citations
20.
Cheney, Nick, et al.. (2017). Evolutionary Developmental Soft Robotics As a Framework to Study Intelligence and Adaptive Behavior in Animals and Plants. Frontiers in Robotics and AI. 4. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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